NDLI: On principal component analysis for high-dimensional XCSR

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On principal component analysis for high-dimensional XCSR

Content Provider	Springer Nature Link
Author	Behdad, Mohammad French, Tim Barone, Luigi Bennamoun, Mohammed
Copyright Year	2012
Abstract	XCSR is an accuracy-based learning classifier system which can handle classification problems with real-value features. However, as the number of features increases, a high classification accuracy comes at the cost of more resources: larger population sizes and longer computational running times. In this paper we investigate PCA-XCSR (a sequential application of PCA and XCSR) in three environments with different characteristics: a discrete and imbalanced environment (KDD’99 network intrusion), a continuous and highly symmetric environment (MiniBooNE), and a highly discrete, highly imbalanced environment (Census/Income (KDD)). These experiments show that in the three different environments, PCA-XCSR, in addition to being able to reduce the computational resources and time requirements of XCSR by approximately 50 %, is able to consistently maintain its high accuracy. In addition to that, it reduces the required population size needed by XCSR. Also, we suggest heuristics for selecting the number of principal components to use when using PCA-XCSR.
Starting Page	129
Ending Page	138
Page Count	10
File Format	PDF
ISSN	18645909
Journal	Evolutionary Intelligence
Volume Number	5
Issue Number	2
e-ISSN	18645917
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-05-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetics-based machine learning Learning classifier systems XCSR Dimensionality reduction Principal components analysis Bioinformatics ApplicationMathematics/Computational Methods of Engineering Applications of Mathematics Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Cognitive Neuroscience Mathematics Computer Vision and Pattern Recognition

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